Procedia Economics and Finance 3 ( 2012 ) 698 – 701

2212-6716 © 2012 The Authors. Published by Elsevier Ltd.Selection and peer review under responsibility of Emerging Markets Queries in Finance and Business local organization.doi: 10.1016/S2212-5671(12)00216-X

Emerging Markets Queries in Finance and Business

Analysis of factors influence the installed capacity of electricityfrom renewable sources in the EU member countries

Carmen-Roxana Dascalua,*aTransilvania University of Brasov, Eroilor Blvd No. 29, Brasov 500036, Romania

Abstract

examines a series of nine socio-economic factors that might influence the installed capacity of electricity fromrenewable sources. The analysis is done to the first ten European countries that registered the most important values of installed power capacity electricity from renewable sources. This econometric analysis leads to conclusion: all socio-economic factors taken for analysis influence the endogenous variables.

© 2012 Published by Elsevier Ltd. Selection and peer-review under responsibility of the EmergingMarkets Queries in Finance and Business local organization

Keywords: European Union, installed capacity, multiple regression, renewable energy

1. Introduction

In the paperaims to analyze factors influencing the installed capacity of electricity from renewable

sources. For analysis were taken first ten EU countries that registered the most important values of installed capacity of electricity from renewable sources. For this analysis will use Student Test, data were obtained using

* Carmen-Roxana Dascalu. Tel.: 0040748459220.E-mail address: roxy_morandau@yahoo.com.

Available online at www.sciencedirect.com

© 2012 The Authors. Published by Elsevier Ltd.Selection and peer review under responsibility of Emerging Markets Queries in Finance and Business local organization.

699 Carmen-Roxana Dascalu / Procedia Economics and Finance 3 ( 2012 ) 698 – 701

Microsoft Office: Excel. In order to achieve the econometric analysis we used two theoretical literature sources: Duguleana L. and Duguleana C, 1998 and Duguleana L., 2002.

Exogenous and endogenous variables are shown in Table 1.

Table 1. Exogenous and endogenous variables, according to according to the installed capacity of electricity from renewable sources

Country Installed capacity of electricity from renewable sources (MWh)

Population HICP inflation rate (%)

GDP per capita in PPS (%)

Unemployment rate (%)

Energy dependence (%)

Net imports of electricity (GWh)

Inequality of income distribution Gini coefficient

Final electricity consumption per capita (KWh per capita)

Electricity prices in households (EUR/100 KWh)*

Germany 45598 81751602 2.50 120 7.50 60.90 -20101 29.30 6392 22.90 Spain 39128 46152925 3.10 99 18.00 81.40 -11039 33.90 5860 16.80 France 30119 65000000 2.30 107 9.50 51.20 -48006 29.80 6772 12.30 Italy 29845 60340328 2.90 101 7.80 85.40 40035 31.20 5180 20.00 Sweden 20491 9415570 1.40 126 8.30 38.00 -1961 24.10 14010 16.50 Austria 16247 8400000 3.60 129 4.80 69.70 4863 26.10 7141 19.10 United Kingdom 10580 62026962 4.50 108 7.60 26.10 11022 25.40 5583 14.10 Portugal 8392 10636979 3.60 77 9.60 83.00 9431 33.70 4554 15.90 Romania 6382 21414000 5.80 49 6.90 27.70 -4248 33.30 1940 9.80 Finland 5008 5375276 3.30 116 8.20 55.00 12772 33.00 15586 12.90

*Real prices, all taxes included 2nd semester, 2009

2. The specificity of model

To study how the installed capacity of electricity from renewable sources may be influenced by population; inflation rate; GDP per capita in PPS; unemployment rate; energy dependence; net electricity imports; unequal income distribution, Gini coefficient; final electricity consumption per capita and household electricity prices, the model can be constructed as:

IC = a0 + a1 Pop + a2 HICP + a3 GDP + a4 UnEm + a5 EnD + a6 Imp + a7 Inc + a8 ElCon + a9 ElPrt + t, Where: IC = Installed capacity of electricity from renewable sources MWh; Pop = Population; HICP = HICP inflation rate %; GDP = GDP per capita in PPS %; UnEm = Unemployment rate %; EnD = Energy dependence %; Imp = Net imports of electricity GWh; Inc = Inequality of income distribution Gini coefficient; ElCon = Final electricity consumption per capita KWh per capita; ElPrt = Electricity prices in households EUR/100 KWh; a0, a1, a2, a3, a4, a5, a6, a7, a8, a9 = model parameters;

t = error specification. Using the method of least squares for estimation of parameters, we obtained the following results: IC = - 455814 - 0.001 Pop 51472,6 HICP + 3764,45 GDP + 1068,86 UnEm 3594,77 EnD + 1,14 Imp + 22238,42 Inc 23,40 ElCon + 449,37 ElPrt + t , R2 = 1 (Table 2); n = 27;

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(.) t Student Table 3. Where: R2 = coefficient of determination; n = number of European Union member countries that have made observations; t Student = Student distribution variation.

Table 2. Regression Statistics

Regression Statistics

Multiple R 1

R Square 1

Adjusted R Square 65535

Standard Error 0

Observations 10

Table 3. Coefficients of the model and Student variables

Coefficients Standard

Error t Stat

Intercept -455814 0 65535

X Variable 1 -0.00122 0 65535

X Variable 2 -51472.6 0 65535

X Variable 3 3764.454 0 65535

X Variable 4 1068.864 0 65535

X Variable 5 -3594.77 0 65535

X Variable 6 1.140411 0 65535

X Variable 7 22238.42 0 65535

X Variable 8 -23.3988 0 65535

X Variable 9 449.3709 0 65535

In the table represents regression analysis Table 2 is observed a coefficient of determination (R2) equal to 1. This value means that the linear model is valid, explaining a 100% variation of installed capacity of electricity from renewable sources.

Applying the Student test, is obtained a theoretical value (t n-k-1 = t0,05/2 27-9-1=17) equal to 2.11, a value which we compared it with the modal values in column t State table 3. Since the modal values of the coefficients a0 a9 in column t State are higher than the theoretical value, the alternative hypothesis H1 is accepted through the estimators are significantly different from 0. In conclusion, we can say that all socio-economic factors taken for analysis influence the endogenous variables: installed capacity of electricity from renewable sources.

Theoretical value of the ration Student (t n-k-1), where: = 100-probability (95%) = 5%, n = number of observations, k = number of

exogenous variables

701 Carmen-Roxana Dascalu / Procedia Economics and Finance 3 ( 2012 ) 698 – 701

Acknowledgements

This paper is supported by the Sectoral Operational Programme Human Resources Development (SOP HRD), financed from the European Social Fund and by the Romanian Government under the project number POSDRU/89/1.5/S/59323.

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